Cascade Skip-Connection BiLSTM Autoencoder for CPR Artifact Removal Prior to AED Shock Advisory.

BACKGROUND

Current automated external defibrillators require pauses in cardiopulmonary resuscitation (CPR) for reliable ECG rhythm analysis, reducing chest compression fraction and delaying defibrillation.

METHODS

We propose a skip-connection BiLSTM autoencoder (SBAE) that removes CPR artifacts directly from one-dimensional ECG signals without auxiliary reference signals or time-frequency transformation. A cascade architecture employs a balanced model for initial denoising followed by biased models whose reconstruction is optimized toward one rhythm class. A conservative routing strategy labels cases where the two stages disagree as indeterminate. Performance was evaluated through 10-fold cross-validation with artifact-level data separation using an FDA-approved shock advisory algorithm.

RESULTS

The cascade SBAE achieved 97.51 ± 1.91% sensitivity for ventricular fibrillation, 94.58 ± 3.39% for ventricular tachycardia, 99.30 ± 0.28% specificity for normal sinus rhythm, 98.02 ± 3.02% for asystole, and 97.21 ± 1.05% for other non-shockable rhythms, exceeding American Heart Association thresholds for all categories. The indeterminate rate was 3.63 ± 1.10%.

CONCLUSION

The proposed framework provides a compact, reference-free solution for shock advisory decision support in automated external defibrillators. Deployment on embedded AED hardware would require additional model optimization and platform-specific engineering.